ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Technical Advances in Plant Science
A Machine Learning Approach for Classifying Date Fruit Varieties at the Rutab Stage
Provisionally accepted
Meshal Alfarhood*
Nawaf AlsahwMohammed AlmajedMeshaal AlzahraniAhmad AlawfiMeshal AlanaziAbdalrahman Alalwan- King Saud University, Riyadh, Saudi Arabia
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Dates have long been a vital part of the cultural and nutritional heritage of arid regions, particularly in the Middle East. Among their various stages of ripening, the "Rutab" stage—an intermediate phase between the "Khalal" (immature) and "Tamar" (fully ripe) stages—holds unique significance in terms of taste, texture, and market value. However, the classification of "Rutab" varieties remains underrepresented in the literature. To address this gap, we present a unique pipeline that leverages machine learning to classify Rutab dates from images. Unlike previous studies that focus primarily on the final ripening stage, our work centers on the underexplored Rutab phase. We collected a custom dataset comprising 1659 images across eight popular Rutab types and evaluated several deep learning models, with YOLOv12 achieving the highest recall of 93%. The proposed system is deployed within a mobile application, with the dual aim of promoting cultural preservation and increasing global awareness of the diversity found within date varieties.
Keywords: Dates, Fruit classification, machine learning, YOLO, Rutab Stage, Image Recognition, Agricultural technology, and MobileApplication
Received: 19 Aug 2025; Accepted: 27 Oct 2025.
Copyright: © 2025 Alfarhood, Alsahw, Almajed, Alzahrani, Alawfi, Alanazi and Alalwan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Meshal Alfarhood, malf@ksu.edu.sa
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